{"id":"W653094652","doi":"","title":"How to keep optimal maintenance strategies with a dynamic optimization approach?","year":2014,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Safety Warnings and Signage","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bombardier (Canada)","funders":"","keywords":"Obsolescence; Computer science; Dynamic Bayesian network; Reliability engineering; Predictive maintenance; Maintenance engineering; Doors; Optimal maintenance; Key (lock); Risk analysis (engineering); Bayesian network; Engineering; Computer security; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002861111,0.0004845363,0.0005202671,0.0002110254,0.000288803,0.0009373323,0.001350374,0.0003934435,0.0002070494],"category_scores_gemma":[0.0004239485,0.0004543467,0.0001682973,0.0004008882,0.0002504682,0.0001650624,0.0007699943,0.0008066468,0.00004149969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001294197,"about_ca_system_score_gemma":0.0001978549,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004641725,"about_ca_topic_score_gemma":0.0003082561,"domain_scores_codex":[0.9940899,0.003297745,0.0004150376,0.00123071,0.000403243,0.0005633586],"domain_scores_gemma":[0.9947495,0.0005205016,0.0005947317,0.002396694,0.001500302,0.0002382538],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003981863,0.001772333,0.0006939546,0.0005642722,0.0006943187,0.00004146158,0.07135203,0.5165426,0.000994836,0.3520879,0.003778343,0.0510798],"study_design_scores_gemma":[0.002739386,0.000009185404,0.005813823,0.003154062,0.0002369111,0.0001059364,0.00810675,0.9548545,0.0007218887,0.001984231,0.01985971,0.002413627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01985747,0.0003176251,0.8767672,0.007673156,0.0002154116,0.0007445388,0.00004327363,0.0003049306,0.09407643],"genre_scores_gemma":[0.6445313,0.00006419962,0.3297046,0.0001442237,0.00002843362,0.0002687947,0.0005868122,0.00008893243,0.02458274],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6246738,"threshold_uncertainty_score":0.9997908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01126113210617508,"score_gpt":0.2352256951039355,"score_spread":0.2239645629977604,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}